Q: What is the main objective of the research presented in the paper?
A: The research develops a new national measure of urban spatial structure, focusing on the built environment through innovative analysis of high-resolution satellite-derived imagery using a convolutional neural network.

Q: What data sources were used for measuring local spatial structure?
A: High-resolution multispectral imagery from the European Space Agency’s Copernicus Sentinel 2 satellites and georeferenced postcodes from the Office for National Statistics.

Q: How was the convolutional neural network model designed and trained?
A: The model, a convolutional autoencoder (CAE), was trained to compress the dimensionality of the satellite data, preserving discriminative features of the area surrounding each GB postcode.

Q: What method was used to represent the salient context of the data?
A: A k-means clustering algorithm was applied to the latent vectors produced by the CAE, identifying postcodes with similar characteristics.

Q: What was the outcome of the national classification of context case study?
A: The study produced a classification covering the full extent of Great Britain, identifying different urban and rural areas with distinct characteristics in the Liverpool City Region.

Q: What are the main conclusions and future research directions from this study?
A: The study introduced a novel method to extract local contextual measures from satellite data. It suggests further exploration in the use of higher resolution data and different geographic extents to improve model performance and representation of urban context.